Published Paper Details:

The Smart Voting System through Facial Recognition using OpenCV and Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) is an advanced, secure, and efficient voting mechanism designed to enhance the accuracy and reliability of electoral systems. Traditional voting systems are prone to issues such as voter impersonation, voter fraud, and inefficiencies in the verification process. This system utilizes facial recognition technology to address these challenges by ensuring that only eligible voters can cast their votes. The proposed system leverages OpenCV for real-time image processing and feature extraction, with HOG being used for identifying facial features. The facial features are then classified using a Support Vector Machine (SVM) model, trained to differentiate between authorized voters and unauthorized individuals. The SVM classifier is trained on facial data, enabling it to achieve high accuracy and robustness in diverse conditions, such as different lighting or angles of faces. The process begins by capturing the voter's face through a webcam or camera at the voting booth. The facial image is then pre-processed, and HOG descriptors are extracted to capture the shape and structure of the face. The descriptors are subsequently input to the SVM classifier, which compares the facial features with a pre-registered database of authorized voters. If the system matches the captured face with the database, the voter is granted permission to vote. This innovative approach improves the efficiency of the voting process, reduces human error, and significantly increases security by preventing fraud or impersonation. Additionally, the system is cost-effective and scalable, making it a viable solution for both small-scale and large-scale elections.